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PHD Perspectives | Turning bluster into business: The year ahead in data and marketing technology

In 2017, we saw the data and marketing technology space continue to make a lot of noise.

If there was an advertising summit, forum or conference you can be sure someone was talking about Leveraging Ad Tech and Monetising Big Data (or maybe Monetising Ad Tech and Leveraging Big Data)!

However, we expect 2018 to be the year when this bluster turns into business. Marketing departments will reflect the industry with ad tech consolidating and centralising, marketing leaders will see the business benefits of getting access to new data sets, and further advancements in machine learning will drive effectiveness and efficiency – so it’s going to be one hell of a year!

With that in mind, here are some things to look out for:

Centralised tech stack teams within marketing departments

2018 will be the year for consolidating multiple data and technology functions into a single, dedicated department.

For digital marketing to be managed and executed correctly, many tools and technologies are required. Historically, many of these have been split into separate silos. This will change this year, with some businesses even going as far as to hire an internal marketing technology manager and/or team. This process will continue with the consolidation of the partners being used. The focus will move from having multiple, separate partners towards a more centralised set-up.

The days of asking, ‘What’s a tech stack?’ are over and brands begin to understand not only the value of marketing technology but the necessity of different tools working together.

Access to new data sets will deliver better quality business insights

In recent years, we have seen the rise of the Data Management Platform; the technology used to store and organise marketing data. The DMP has slowly started to surface into becoming a core function of a digital marketer’s technology stack. However, collecting data within a DMP is a great start, but only a small part of what is truly required.

There is now a key requirement that all data is not only stored within the DMP, but surfaced, and made actionable. Having all a brand’s data collected and mapped together allows marketers to gain insight and analysis never possible before and show the true value of what ‘big data’ can deliver. To enable this, companies will not only need to create a central marketing technology function, but develop deeper relationships with cross function teams, such as IT. This will lead to concrete business benefits. For example, car insurance companies now use data from apps to gauge how well customers drive, and use that to determine premiums.

So, marketing leaders will begin to ask more and more questions about data and its benefits. Questions such as ‘what’s Microsoft Azure?’ or ‘should we be pushing that into the Data Lake?’ will become more frequent. Understanding of how centralised data will truly begin to change the way we think about digital marketing is now here.

The machines are still coming, but they need human direction.

Last year there were many articles worrying about Artificial Intelligence. Within marketing technology, we have been talking about algorithms for years. In marketing, we often look to machines to find patterns and values. However, without humans to enable these machines, to point them in the right direction and add some context, machine learning won’t deliver on its potential.

Therefore, we will continue to see a rise in machine learning in 2018, but what will become just as important is the way we use this. What questions we ask the machines to work on, and the parameters we put around those, will deliver radical new insights. Advanced Data Science teams will develop and expand, along with new products such as Custom Valuation, also known as Bring Your Own Algorithm. This will enable us to develop client-specific automated buying models and valuations, driving efficiency and effectiveness tailored to each brand’s unique needs.

Conclusion

In recent years many organisations have spent time understanding what can be done with data and gaining entry level experience. However, in 2018 we will see brands;

Create central tech stack teams to better align internally

Gain access to new data sets to drive better quality business insights

Build advanced Data Science teams to leverage the efficiency and effectiveness possible from machine learning 2018 should be an educational but actionable year for brands.